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Record W2259147899

Implications of increasing demand for freshwater use from the water footprint of irrigated potato production in Alberta.

2014· article· en· W2259147899 on OpenAlexaboutno aff
A. Moe, K. Koehler-Munro, Robert C Bryan, T. Goddard, L. Kryzanowski, R. Schenck, D. Huizen

Bibliographic record

VenueProceedings of the 9th International Conference on Life Cycle Assessment in the Agri-Food Sector (LCA Food 2014), San Francisco, California, USA, 8-10 October, 2014. · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicWater-Energy-Food Nexus Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIrrigationWater useEnvironmental scienceWater resource managementAgricultureWater resourcesFarm waterWater scarcityDeficit irrigationClimate changeWater conservationIrrigation managementGeographyAgronomyEcology
DOInot available

Abstract

fetched live from OpenAlex

Freshwater use has become a major social and environmental concern in the last two decades. This is due to rising food demand, rapid urbanization, industrial development and climate change which are significantly increasing pressure on freshwater resources (Ridoutt and Pfister, 2010, Gheewala et al., 2013). Agriculture is one of the largest users of global freshwater resources, accounting for about 70% of freshwater withdrawals as irrigation (WWAP, 2009). Irrigation accounts for 84% of the total water use in South Saskatchewan River Basin (SSRB) in Alberta (AMEC 2009). Irrigation water use is competing with other demands for freshwater such as household and industrial consumption. A projection of water supply study for the SSRB in Alberta forecasts that water use in the SSRB will increase 53% from the current 1,981,000 dam3 to about 3,040,000 dam3 by 2030 mainly due to expansion of irrigation districts (AMEC 2009). A significant increase in water use will affect a ratio of total fresh water withdrawals to hydrological availability (WTA) leading to a greater water stress index (WSI) in Alberta. The study aims to assess the water footprint of irrigated potato production in Alberta for two scenarios

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.162
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.027
GPT teacher head0.254
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the 9th International Conference on Life Cycle Assessment in the Agri-Food Sector (LCA Food 2014), San Francisco, California, USA, 8-10 October, 2014.Same topicWater-Energy-Food Nexus StudiesFrench-language works237,207